Item counting in guided information access systems

ABSTRACT

Methods of counting the number of matching items (more generally finding objects) indirectly associated with each detail selector (more generally another object) available for selection in a database search system or generally useful in other applications are disclosed. In some embodiment the method uses a set of matching items and traverses to each indirectly associated detail selector via a traversal through each associated entity, and a traversal through each detail selector associated with that entity. The method is easily extendable to the case of multiple entity levels. In some embodiments the method first creates an association matrix directly associating items to selectors and allowing item determination or counting more simply.

BACKGROUND

1. Technical Field

The present disclosure relates generally to improvements in databasescomprising structured and unstructured data. More specifically itdescribes methods of improving the associative information shown to theuser and dynamically adjusted during user's navigation through theinformation in the data.

2. Background

Access to information in databases has recently been made much moreconvenient by what is called by some, Faceted Navigation and by othersGuided Information Access or GIA. In simplified terms GIA allows usersto choose terms (called selectors) to describe the data items ofinterest which are then made available as matching items. The selectorscan be either descriptive of the contents of data items, or termspresent in the content, or both.

One of the very useful features of GIA is that the selectors availableto the user for choosing are always adjusted to make sure that the useralways chooses only selectors which, combined with those previouslychosen, guarantee at least one matching item. Another very usefulfeature of GIA is that the counts of items associated with each selectorcan be displayed and adjusted to be always current as the user narrowsthe description with additional selector choices.

In Faceted Navigation systems, selectors are directly associated withitems and used to select (find) matching items. When items containinformation about multiple entities of the same kind, such as multiplepeople, vehicles, places etc., descriptions of items using selectors,directly associated with items, may find items that do not contain thedescribed entities. For example, in a database of incidents, where anyincident may involve multiple people, if you choose a person's eye coloras brown and a person's ethnicity as European, and both selectors areassociated directly with items, you will find items with such a person(if they exist) but you will also find items in which there are two ormore people, one of which has brown eyes and a different person who isEuropean. This is the data ambiguity problem. This problem is solved inGIA by using entities. When a user chooses selectors, Technology forInformation Engineering (TIE), the server technology enabling GIA,matches entities and then those matched entities match the items whichinclude those entities. Sometimes multiple levels of entities may beneeded. For example, when in a single item multiple people have multipleaddresses, each address can be an entity which is directly associatedwith the person entity.

When, for example, an item is an incident involving multiple people andthe selectors are terms describing the people, the shown countsassociated with each selector can be either counts of people, or countsof items, or both. So for example, in the case when a total of 100matching items contains a cumulative total of 26 people (the entities)the selector Brown Eyes may show a count of 13 entities (which meanspeople) and 10 items (meaning incidents), meaning that there are 13people with brown eyes and they are distributed amongst 10 incidents.

The counts of items can show the user how many items involve aparticular entity description. So for example if the items are incidentsof crimes each associated with a selector descriptive of the type ofcrime, each involving some number of people, it would be very useful tosee how many crimes of each type occurred. Each crime type is describedby a selector, and the item counts associated with each selector arethen counts of the respective incidents or items involving that crimetype.

The calculation of entities (such as people) associated with eachselector is a relatively fast and simple task for a computer program toperform. However, the evaluation of the item counts is a more timeconsuming computer task and so requires additional effort in the designof the methods to achieve a suitably fast, efficient response. Suchcalculations have to be performed after each user choice of a selector.A fast response is very desirable because the user would beinconvenienced if after each choice of a selector the response isappreciably delayed. It is the objective of this present disclosure todescribe suitably fast systems, methods, and non-transitorycomputer-readable storage media for counting items in this and anysimilar contexts.

SUMMARY

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Disclosed are systems, methods, and non-transitory computer-readablestorage media for providing counts of items associated with eachselector in a GIA search interface, in detail selector groups usingintermediary selector associations with entities. For example, in acriminal database, a user can search for a particular item in adatabase, i.e., an incident report, which is characterized by detailssuch as physical characteristics of each person described in the report,characteristics of each car, details of each crime, etc. In such aninstance, each person, car, and crime may need to be treated asentities. Each person, car, and crime has additional details orcharacteristics that describe them and each of those details of theentities can be described by a detail selector and appear in theincident report.

Continuing with the preceding example, if a user of a criminal databasewanted to search for an incident report based on a robbery committed bya person, driving a particular car, the user could be presented with aplurality of detail selectors describing details of entities (such aspersons and cars) associated with the incident report. In general, whenitems contain structure, that is they contain data about multipleentities of the same kind (such as people, vehicles etc.), theassociations of selectors directly with items will not distinguishbetween entities of the same kind present in the same item, causing dataambiguity. This problem is solved by using entities.

Using entities creates an additional level of complexity for many datafunctions. One such function, that is common in GIA systems, iscalculation of item counts. Item counts identify the number of currentlymatching items in a database associated with a displayed detailselector. Since entities create an intermediate relationships between adetail selector and an item, the presence of the entities complicatesthe calculation of the counts of items because they are indirectlyrather than directly associated with selectors. For items directlyassociated with detail selectors, the counting process iscomputationally simple because it traverses each item and for each itemthe associated selectors, incrementing the selector count of each suchselector.

Two item counting method embodiments, for implementations using GIA aredescribed. The first method, called the multiple matrix method, uses twoor more association matrices. In the simplest case of just one level ofentities, the Item-Entity (item to entity) matrix and the Entity-DetailSelector (entity to detail selector) matrix. When more levels ofentities are present, more corresponding association matrices are used.The second method, called the single matrix method, converts themultiple association matrices to a single direct association matrixbetween items and detail selectors and uses that for item counting.

Counts of items or entities associated with each selector comprise the,so called, reverse query. Counts of items associated with each selectorare often performed even when displaying them may not be required,because the extra effort required is not significant when the counts arethose of items in bare groups, or the counts are of entities, instead ofitems, in entity groups. However, evaluating the counts of associateditems in entity groups does require significant extra effort. Themultiple matrix method can be used to perform such item counts duringthe evaluation of the reverse query.

The reverse query is calculated using the results of the forward query.In a system using entities, the forward query evaluates the Booleanquery of selectors the result of which is a set of matching entities anda set of matching items. The matching items are found by evaluating acomputer generated Boolean query comprised of the matching entities.Evaluation of the item counts and optionally entity counts, associatedwith each selector can be performed using the following method steps.

Starting with the matching items set, iterate through each item, andperform the following steps for each item, using a Result List OfSelectors' Counts (RLSC) to store counts of items associated with eachselector:

determine the subset of entities, associated with the current item, callit the entity subset (ES);

iterate through each entity member of the ES, and for each currententity, determine the subset of selectors associated with it. For eachassociated selector in this subset of selectors:

increment by one the item count in the item count column of the RLSC,provided the ID of the item being processed is not one that has alreadybeen counted as associated with that selector.

The conditional in the last listed step can be achieved by using twoelements for each selector ID in the RLSC: the first storing the itemcount, the second storing the ID of the item which contributed last tothe count. Then when determining whether to increment the countassociated with a selector, the last contributed item ID, as stored inthe second element of RLSC for that selector, is compared with thecurrent one. Unless those two are the same, the increment in the countis made and the current item ID is written to the second element of theRLSC for that selector. If the two are the same, no change is made andthe next selector is checked.

A method for computation of entity counts could be performed as follows:

determine that set of entities, the associated entities set, each ofwhich is associated with at least one of the matching items;

for each entity in the associated entities set, determine the associatedselectors and increment the entity count of each.

The single matrix method of item count evaluation needs only thedescription of the creation of the single matrix from multiple matrices.Once this is created, item counts are evaluated using the same steps asin the case of no entities present, which is the same as the calculationof entities described above.

In general terms the single matrix is the binary product (or Booleanconjunction) of the two matrices: the Item-Entity and the Entity-DetailSelector.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1 illustrates an association graph of an exemplary database havingdetail selectors with direct and indirect associations to items;

FIG. 2 illustrates a flow chart of an exemplary method for counting thenumber of matching items for each associated detail selector;

FIG. 3A illustrates exemplary data structures used for item counting;

FIG. 3B illustrates exemplary data structures used for item counting;

FIG. 3C illustrates exemplary data structures used for item counting andfor representing data associations in databases;

FIG. 3D illustrates exemplary data structures used for item counting andfor representing data associations in databases;

FIG. 3E illustrates exemplary data structures used for item counting andfor representing data associations in databases;

FIG. 3F illustrates exemplary data structures used for item counting andfor representing data associations in databases;

FIG. 3G illustrates an association graph of an exemplary database havingdetail selectors with direct and indirect associations to items;

FIG. 3H illustrates exemplary data structures used for item counting andfor representing data associations in databases;

FIG. 3I illustrates exemplary data structures used for item counting andfor representing data associations in databases;

FIG. 4 illustrates an exemplary criminal database search according toone embodiment;

FIG. 5 illustrates an exemplary single matrix of items to detailselectors derived from the product of the two example matrices of detailselectors to entities and entities to items; and

FIG. 6 illustrates an example system embodiment.

DETAILED DESCRIPTION

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.

The present disclosure addresses the need in the art for a databasesearch interface that employs intermediate associations between itemsand selectors. Methods are disclosed for searching databases with theuse of such intermediate associations. Such methods may preferablyassist a user in information navigation though databases by displayingadditional information about the data associations. For example,additional information may be derived from the process and madeavailable to the user as part of the information navigation. Thus, auser (a person, entity, computer, computer program, etc.) may beinteractively guided to updated derived information relating to theinformation he/she/it is searching.

To begin with, the following table offers some exemplary meanings ofsome terms.

item, entity, detail any data. selector each generally referred to as anelement. list a collection of elements implemented in any convenientstructure. query a single element. a plurality of elements and Booleanoperators representing a logical expression. selection or choosing byany person or computer instruction in a selecting computer system. userany person or computer system, including part of any system, using themethods. A different user is possible for each instance of the use ofthe term.In all methods described herein it is understood that computer softwareimplementations of such methods may use unique identifiers, for examplesimple integers, to reference any element. When a method refers to anelement, such a reference must be understood as optionally exactlyequivalent to a unique identifier of said element. At times softwareimplementations may use a unique identifier as a reference to thelocation of the true unique identifier of an element. These too shouldbe considered exact equivalents to the element itself.

The present disclosure may employ a search method where a user canselect from a list of available categorical descriptions,characteristics, or other search input (generally referred to as “detailselectors”) that, for example, describe the database entry(ies) or itemshe/she/it is looking for. Any resulting search entries from selectingone or more detail selectors are referred to as “matching items.” Eachdetail selector reveals or describes one or more details about amatching item and may be selected by the user when performing a searchof items with that detail. One exemplary embodiment is a criminaldatabase where the detail selectors may include descriptions of age,height, weight, race, hair color, eye color, the crime committed, thedate of the crime, and other details associated with the criminalrecords that are stored therein.

Detail selectors may also describe objects that are a part of the datain the items; these objects are known as “entities.” In general, anentity can be a representation of an object, article, body, or assemblyof data parts, etc. Examples of an entity include a person, a vehicle,an address, or even a sentence as represented by a group of unique words(all words in the sentence without duplicates). Referring back to thecriminal database example above, an entity may be, for example, a personin a criminal report, a vehicle in an accident report, or a gun. As seenby the example graph of the associations represented in FIG. 1, anentity 112 can be an intermediate node between a detail selector 102 andan item 126, where the entity 112 is directly linked to the detailselector 102 and also to the item 126. An entity 112 represents anintermediate relationship between a detail selector 102 and an item 126.As such, entities indirectly link together (detail) selectors and items.Such work to establish entities in database association structures hasbeen disclosed by application Ser. No. 12/223,275 filed on Jan. 25, 2007and published as U.S. 2010/0241649 (“the '649 publication”), and isincorporated by reference herein in its entirety.

Entities add another level of complexity in the associations metadatabut allow for more efficient and accurate information navigation byproviding additional related information and dynamically keeping itupdated during navigation.

In some preferred embodiments, when a user of a database searchinterface chooses to select a detail selector or multiple detailselectors, the method finds all matching items associated with thedetail selector or with the Boolean comprised of multiple detailselectors, and calculates and provides the number of matching itemsassociated with each of the detail selectors in the database, usuallyalso disabling from further choices selectors whose item counts arezero. These embodiments preferably calculate and provide these numbersas the user is performing his/her selection and does so on averageresponding in about one second or less, though sometimes, for largerdatabases and responses returning very long lists of selectors, theresponse time may be longer (measured from the time the user makes aselection to the time the numbers are available to the user). Thisenables the user to immediately see precisely how many matching itemsresult if he/she were to choose and select a certain additional detailselector without even sending a further request or query.

Entities were first introduced in the '649 publication to solve the dataambiguity problem. To explain how entities function, a small portion ofan example database association graph illustrated in FIG. 1 will beused. Referring to FIG. 1, the database contains detail selectors d₁102, d₂ 104, d₃ 106, d₄ 108, d₅ 110; entities e₁ 112, e₂ 114, e₃ 116, e₄118, e₅ 120; and items i₁ 122, i₂ 124, i₃ 126, i₄ 128, i₅ 130. Theconnecting lines between the detail selectors, entities, and itemsrepresent the associations. When a user chooses, inputs, and/or selectscertain detail selectors, for example d₁ 102 and d₂ 104, the TIE systemwill automatically create and then evaluate the forward query. Assumingthe selectors were automatically combined conjunctively in the query,the matching entities would be e₂ 114 and e₃ 116. Then the matchingitems are found by effectively creating and evaluating a query comprisedof entities. In this example, the query searches for all items that areassociated with any of the matching entities, in this case e₂ 114 and e₃116. This gives the matching items as i₁ 122, i₃ 126, and i₅ 130. Thenthe identified matching items and matching entities are used toautomatically perform reverse queries. There are two possible reversequeries and two corresponding results: one using the matching entities,which we will call the short response the other using the matching itemswhich we will call the long response. The first finds all (detail)selectors associated with any one of the matching entities and in thisexample it is the set d₁ 102, d₂ 104, and d₃ 106. The second, as a firststep, finds all entities associated with the matching items. In thisexample, it is e₁ 112, e₂ 114, e₃ 116, e₄ 118, e₅ 120, and as the secondstep, finds all selectors associated with at least one of theseentities, which, in this example, gives the set of selectors d₁ 102, d₂104, d₃ 106, d₄ 108. The item counts calculations of each selector inboth the short and the long responses are the same. The short responselist of selectors is shown, or enabled in all detail selector groups inthe subject entity group, when the user is in the editing state of thatentity. Editing state means that the user can edit their choice ofselectors describing the current entity. The long response list ofselectors is shown, or enabled when the user indicates he/she hasfinished describing the current entity and wishes to narrow the matchingitems further by describing another entity to be matched in thecurrently matching items. It is also used in all entity groups that arenot in the editing state. Lists of detail selectors available for theuser to choose from are updated to the corresponding detail selectors.In this example, if the user were to continue in the entity editingstate, GIA would allow him/her to choose from the detail selectors d₁102, d₂ 104, and d₃ 106. After the he/she indicates the desire todescribe another entity, having completed the editing of the currentone, GIA would identify detail selectors d₃ 106 and d₄ 108 (in additionto d₁ 102 and d₂ 104) as having an association with the identifiedmatching items i₁ 122, i₃ 126, and i₅ 130 and make them available foradditional entity description. The calculated item counts, using themethods described here, could be made available to the user in eachcase.

When providing related detail selectors for subsequent selection, someembodiments calculate and provide the number of matching items andadditionally or alternatively the number of matching entities for eachdetail selector that may subsequently be chosen. FIG. 2 illustrates aflow chart of an exemplary method for counting matching items associatedwith each detail selector that may subsequently be selected. Referringto FIG. 2, after each time a user selects a detail selector, the methodprepares for item counting in the initialization step 202 by setting theItem Count (304 in FIG. 3A) and Contributing Item Field (306 in FIG. 3A)for every detail selector to a numerical “0” and a “NULL,” respectively.Then with a given set of matching items already identified as matchingthe forward query comprised of the user-selected detail selectors (e.g.the method gets a matching item list/set 204), the method iteratesthrough the matching items. Choosing the next item i_(next) 206, themethod temporarily stores and retains the item identification (i.e.“i_(next)”) of this next item i_(next) and traverses through all of thedetail selectors directly and indirectly associated with the itemi_(next). This ensures that every relevant detail selector (i.e. thoseassociated with a matching item) is updated with the appropriate itemcount, where an intermediate entity exists between an item and a detailselector.

In a current entity group which is in the editing state, only the detailselectors associated with matching entities and any bare group selectorsmatching items (i.e. only those detail selectors in the “short list”)are made available to the user for subsequent selection. The detailselectors associated with matching items but not necessarily withmatching entities (i.e. those detail selectors unique to the “longlist”) are made available to the user in all entity groups not in theediting state.

Direct and Indirect Associations

FIG. 1 shows a detail selector (i.e. d₅ 110) that is directly associatedwith an item (i.e. i₄ 128). A detail selector is directly associatedwith an item, or alternatively an item is directly associated with adetail selector, if they are linked together without using any othernodes in the link between them. Selectors directly associated with itemsare by definition in a bare group. A detail selector (for example, d₁102) is indirectly associated with an item (i.e. i₅ 130), oralternatively an item (i.e. i₅ 130) is indirectly associated with adetail selector (i.e. d₁ 102), if there is at least one node (i.e.entities e₂ 114 and e₃ 116) in the link between them. Each additionlevel/line (i.e. line of entities) in between detail selectors and itemsallows more information to be coupled with the levels/lines above (i.e.detail selectors) and below (i.e. items, or other entities). Often theuser does not actually see or know of the entities in between the detailselectors and items, but in some cases it can be very beneficial for theuser to be aware of them, such as the case where entities representpersons associated with crime incidents/police reports. In other casesit may be useful to give the user control of entities in formulating thequery.

Vectors/Matrices Establish Data Structure and Links:

Associations between detail selectors and entities, as also entities anditems, or directly between detail selectors and items, can be usefullyvisualized as three binary valued matrices, or tables sometimes alsocalled bitmaps. For example, the Detail Selector-Item association matrixwould be a matrix in which each row represents a detail selector andeach column an item. An example 330 is depicted in FIG. 3C, in which anexisting association between selector 316 d _(j) and item 318 i _(g) isstored as the value 1 in the cell 336 which is in row j and column g. Ifthe association between these was not present, then cell 336 would storethe value 0. The total matrix of zeros and ones is thus a binary valuedmatrix or bit-map. Each row and column of such a matrix is called thecorresponding bit vector. Fox example, in the Detail Selector-Itemmatrix, the row vector is called the detail selector vector and thecolumn vector is the item vector. The whole matrix is usually stored asan array of vectors, though not necessarily as bit-vectors. Moreefficiently each vector is stored as an array of sorted IDs of thecorresponding associated elements. So for example, each detail selectorvector is stored as an array of sorted numbers, each numbercorresponding to the column number of the item (which is usually alsoits ID) associated with the detail selector.

FIG. 3A and FIG. 3B show possible data structures for implementing themethods of some embodiments. For traversing through directly associateddetail selectors, item vectors (Item-Detail Selector vectors) 320 may beused, as illustrated in FIG. 3D. For each of n item vectors 320 (i.e.one vector for each of items i₁ through i_(n)), there are m cells 322for m detail selectors. If i_(g) 318 is directly associated with d_(j)316, then slot number j 326 in i_(g)'s item-detail selector vector 324will hold a binary “1” 328. If i_(g) 318 and d_(j) 316 are not directlyassociated, then there will be a binary “0.” These vectors are createdand set up with the appropriate binaries (“1” or “0” depending on theexistence of direct associations) prior to the item counting.

In systems using entities, there are also often detail selectors that donot associate with any entities, such as the example in FIG. 1 of theassociation between d₅ 110 and i₄ 128. Such detail selectors are said tobelong to a bare group. They describe the whole data item rather thansome part of it. The association of such bare group detail selectors canbe stored in a (direct association) detail selector-item matrix, asillustrated in FIG. 3C.

Associations of detail selectors with entities and then those entitieswith items are normally represented by two matrices, exemplified bymatrices 352 and 360 in FIG. 3E and FIG. 3F, respectively. However, fordetermining associated item counts and other evaluations of the reversequery, it is possible to condense the two matrices into one (indirectassociation) detail selector-item matrix (e.g. product matrix) showingthe association of detail selectors with items through entities. Such amatrix cannot be used for determining the items matching a Boolean querycomprised of details selectors because of data ambiguity alreadydescribed. However the matrix can be used for an alternative method ofcalculating item counts associated with each available selector. Usingthis condensed matrix is more efficient than the use of the twomatrices, but requires the extra initialization time to create such amatrix from the two and may also require additional RAM.

Referring back to FIG. 3C, the detail selector-item matrix maypreferably be created and set up before the item counting takes place.From the above description details, one having ordinary skill in the artwould know how the method uses these vectors and/or matrices todetermine associations for traversing. However, it should be noted thatthe method is not limited to using these vectors and matrices and can beimplemented in other similar ways.

FIGS. 3E, 3F, 3H, and 3I illustrate the use of vectors and matrices thatmay be used when traversing through indirectly associated detailselectors by way of entities to evaluate query results. The use ofentities in GIA introduces an additional dimension of complexity andintricacy while supporting the search for multiple entities withinitems. For each additional level/line (i.e. line of entities) between aline of items and a line of detail selectors, there are additionalvectors, matrices, and/or matrix dimensions. As mentioned previously,additional levels/lines of intermediate entities may allow for searchesof items which contain second level entities, i.e. entities that in turnare contained within other entities, and distinguish between multiplesecond level entities within an item.

In FIG. 3G, since item i_(d) 314 is indirectly associated with detailselector d_(e) 310 through entity e_(f) 312, then there will need to bean item-entity vector 337 for i_(d) (see FIG. 3H) and an entity-detailselector vector 344 for e_(f) (see FIG. 3I). In FIG. 3H, the item-entityvectors will have k memory cells 342 for k entities. For example, memoryslot number f 338 will hold a binary “1” 340 because i_(d) 314 and e_(f)312 are directly associated. This is one way of representing that itemi_(d) and entity e_(f) are directly associated. Moreover, in FIG. 3I,the entity-detail selector vector 344 for e_(f) 312 will have m memorycells 350 for m number of detail selectors. Memory cell e 346 will holda binary “1” 348 because entity e_(f) 312 and detail selector d_(e) 310are directly associated. The above vectors are preferably establishedbefore the item counting occurs.

These vectors can also be parts of matrices. In the example depicted inFIGS. 3E, 3F, 3H, 3I, there are two matrices, an entity-item matrix 352(FIG. 3E) and a detail selector-entity matrix 360 (FIG. 3F). Item-Entityvectors (FIG. 3H) can be the columns 356 to form the entity-item matrix352 (FIG. 3E). Entity-Detail Selector vectors (FIG. 3I) can be thecolumns 364 to form the detail selector-entity matrix 360 (FIG. 3F). Ifitem i_(d) 314 and entity e_(f) 312 are directly associated (FIG. 3G),then the memory cell at row f and column d (memory slot [f,d] 358) inthe entity-item matrix 352 (FIG. 3E) will hold a binary “1,” otherwise a“0.” Similarly, if entity e_(f) 312 and detail selector d_(e) 310 aredirectly associated (FIG. 3G), then the memory slot at row e and columnf (memory slot [e,f] 366) in the detail selector-entity matrix 360 (FIG.3F) will hold a binary “1,” otherwise a “0.” These matrices are alsopreferably established before the item counting takes place to avoidunduly increasing response time. One having ordinary skill in the artwould know from the descriptions presented here how the method usesthese vectors and matrices to determine associations for traversing.However, it should be noted that the method is not limited to usingthese vectors and matrices and can be implemented in other similar ways.

For example, the detail selector-item matrix (e.g. as shown FIG. 3C) canbe and preferably is implemented as an array of detail selector vectorsfor quick access to each vector. Alternatively, or additionally, thesame matrix can be implemented as an array of item vectors (e.g. asshown in FIG. 3D) for quick access to each vector. Each vector can, forexample be implemented as an array of bits, each either zero or 1, or asan array of (preferably sorted) integers, where each integer is thearray index of a non-zero bit in the array of bits implementation. Eachvector can be stored compressed using any useful compression method(s).

Evaluating Item Count for Each Associated Detail Selector

FIG. 3A illustrates the structure of the detail selector item count(DSIC) table 308 used to store item counts of items associatedindirectly (through entities) with (detail) selectors. Each rowidentifies a detail selector and the first column 304 stores the itemcount (or tally) while the second column 306 stores the lastcount-contributing item's identification. Table 302 in FIG. 3B is usedfor storing the item counts of selectors directly associated with items,that is of selectors in the bare group.

Referring back to FIG. 2, FIG. 3A, and FIG. 3B, each time the associateditem counts need to be evaluated, the initialization process 202 startswith each selector's contributing item ID element 306 (in DSIC table308) storing some indicator equivalent to a “NULL” and each item countelement 304 (in DSIC table 308) and 303 (in BGIC table 302) storingzero. For a given set of matching items (matched by a Boolean expressioncomprising the chosen detail selectors), the method gets the matchingitem list at 204, iterates to the next item at 206, and for each item,iterates through all of the objects (either entities or selectors)associated with the item at 208, determines if the associated object isan entity at 212 (and so indirectly associated with selectors), or aselector directly associated with the item. If the current item(i_(current)) is directly associated with selectors (result of query 212is “No”; the current object is a selector), then the currentobject's/selector's count 303 in table 302 of FIG. 3B (Bare Group ItemCount Table) is incremented 210. If the current object (e.g. currentselector) is not the last object 222, the method moves on to the nextobject 208 and continues this process. If the current object is the last222, then the method determines whether the current item is the lastitem 224; if not, the method iterates to the next item 210 andcontinues; if so, the method is complete 226.

If the current item (i_(current)) associated with entities (result ofquery 212 is “Yes”; the current object is an entity), and so indirectlyassociated with detail selectors, the method iterates to the next detailselector 214 associated with the current object/entity. Next it isdetermined, at 216, if the previously contributing item ID in DSIC 308(“i_(previous)”) for the current detail selector is different fromi_(current). If the contributing item's ID (“i_(previous)”) is differentfrom that of i_(current), the current detail selector's contributingitem field is updated to the identification of i_(current) (e.g.“i_(current)”) and the current detail selector's item count/tally willbe incremented at 218. If, however, the contributing item's ID(“i_(previous)”) is the same as that of i_(current), the contributingitem field and the item count/tally of the current detail selector willnot be altered. If the current detail selector is not the last 220, thenthe next detail selector will be processed at 214. If the currentselector is the last 220, then the method determines whether or not thecurrent object (e.g. current entity) is the last object 222. If not, themethod moves on to the next object 208 and continues this process. Ifthe current object is the last 222, then the method determines whetherthe current item is the last item 224; if not, the method iterates tothe next item 210 and continues; if so, the method is complete 226. Inthis way, the iteration process continues through all matching items.This process ensures that a single matching item is not counted morethan once.

When all detail selectors directly and indirectly associated with eachof the matching items have been traversed and their respective itemcounts updated, the method provides matching item counts for each of the“long list” detail selectors (those associated with the matching itemsthrough the entities). In most enterprise databases, the method providesthe user with the complete response, generally in a second or less, anddoes so following each detail selector chosen by the user.

FIG. 4 illustrates an exemplary method as applied to searching in acrime database. In this example, the detail selectors d₁ through d₆(402, 404, 406, 408, 410, 412) describe characteristics of the people(i.e. suspects, criminals), each of whom is denoted by an entity, frome₁ to e₆ (414, 416, 418, 420, 422, 424). The people, or entities 414,416, 418, 420, 422, 424, are associated with police reports/crimeincidents, which are denoted by items i₁ through i₆ (426, 428, 430, 432,434, 436). Although vectors or other similar approaches could be taken,in this example a detail selector-entity matrix 438 stores the directassociations between the detail selectors 402, 404, 406, 408, 410, 412and the entities 414, 416, 418, 420, 422, 424, while an entity-itemmatrix 440 stores the direct associations between the entities 414, 416,418, 420, 422, 424 and the items 426, 428, 430, 432, 434, 436.

This example briefly illustrates the notion of detail selector groups.Each detail selector d₁ to d₆ (402, 404, 406, 408, 410, 412) actuallybelongs to its own respective detail selector group, which usuallycontains a multitude of detail selectors. For the purposes ofsimplicity, this example only shows one detail selector from sixdifferent detail selector groups (sex, age, height, weight, ethnicity,eye color). As shown in the graph representation, the detail selector d₁“Male” 402 from the detail selector group “Sex” is directly associatedwith entities e₁ “Abe” 414, e₂ “Ben” 416, and e₅ “Ed” 422. The otherpeople's sexes are either not “Male” or unknown to the database at thistime. (For purposes of simplicity, detail selectors “Female,” “Other,”“Unknown,” etc. within the detail selector group “Sex” are not shown inthis example.) Because detail selector d₁ “Male” 402 is directlyassociated with entities e₁ “Abe” 414, e₂ “Ben” 416, and e₅ “Ed” 422,the memory slots at row 1 and columns 1, 2, and 5 in the detailselector-entity matrix 438 will each have a binary “1” while the othercolumns in row 1 will each have a binary “0.”

Furthermore, as illustrated in the graph representation, the entity e₁“Abe” 414 is directly associated with items i₁ “Robbery 1/1/01” 426 andi₃ “Fraud 3/3/03” 430. This means that “Abe” was involved with a robberyincident on 1/1/01 and a fraud incident on 3/3/03, but not with theother reports/crimes to the knowledge of the database. As such, thememory slots at row 1 and columns 1 and 3 in entity-item matrix 440 willeach hold a binary “1” while the other columns in row 1 each hold abinary “0.” Again, vectors or other similar approaches rather thanmatrices could be used instead.

A user of such a database, such as a police officer, may want to findcertain suspect(s) or criminal(s) associated with a certain incident(s)or crime(s), but may not know or remember much about the suspect(s) orincident(s). For example, the officer may only know that a 35 year-oldmale was involved. When the officer chooses and selects detail selectorsd₁ “Sex: Male” 402 and d₂ “Age: 35” 404, the method performs a forwardquery, identifying all the directly and indirectly associated matchingitems (i.e., all criminal reports referring to a 35 year-old male). Themethod may first search the (direct association) detail selector-itemmatrix 442 for any directly associated items. There are none in thisparticular example; each of the memory slots in the detail selector-itemmatrix 442 hold a binary “0.” The method may next search the detailselector-entity matrix 438 for entities that are directly related to theconjunction of the selected detail selectors, comparing d₁ 402 and d₂404 (“Sex: Male” and “Age: 35”) to determine whether they share anycolumns having “1” (i.e. whether the conjunction of d₁ 402 and d₂ 404result in any matching entities). Since d₁ 402 is directly associatedwith e₁ 414, e₂ 416, and e₅ 422, and since d₂ 404 is directly associatedwith e₁ 414 and e₂ 416, the conjunction of d₁ 402 and d₂ 404 will resultin matching entities e₁ 414 and e₂ 416. This means that in this databaseonly e₁ “Abe” 414 and e₂ “Ben” 416 are both male and 35 years-old.

As can be seen in entity-item matrix 440, (matching) entities e₁ 414 ande₂ 416 are also disjunctively directly associated with items i₁ 426, i₂428, and i₃ 430 because e₁ 414 is directly associated with i₁ 426 and i₃430, and e₂ 416 is directly associated with i₁ 426 and i₂ 428. Theseitems i₁ “Robbery 1/1/01” 426, i₂ “Murder 2/2/02” 428, and i₃ “Fraud3/3/03” 430 are thus the matching items resulting from the officer'schoice of detail selectors d₁ “Sex: Male” 402 and d₂ “Age: 35” 404. Thismeans that by selecting d₁ “Sex: Male” 402 and d₂ “Age: 35” 404, theofficer will have located three crime incidents/police reports, oneabout a robbery on 1/1/01 (i₁ 426), another about a murder on 2/2/02 (i₂428), and a third one about a fraudulent act on 3/3/03 (i₃ 430), aftersending a request to the database server.

Referring to FIG. 2 and FIG. 4, with the matching items found (gettingthe list of matching items) 204, the method can now proceed to calculatethe item count/tally for each detail selector associated with thesematching items 204. The item counting process will tell the officerwhich detail selectors are associated with these three matching itemsand are available for selection, because the only available selectorsare those whose associated item counts are non-zero. The process willalso tell the officer how many matching items will result if he/she wereto subsequently choose/select one or more of these other availabledetail selectors. The officer can then select from these other availabledetail selectors to narrow or refine his/her search.

Proceeding with the item counting process, the method then performs areverse query by traversing back from i₁ 426 to all of its directly andindirectly associated detail selectors. The method checks for any detailselectors directly associated with i₁ 426 (i.e. the next objectassociated with the current item i₁ is not an entity, at query 212)using the detail selector-item matrix 442, and, if any, increments theitem count 210 of the current selector. In this example, there are nonebecause each of the memory slots in the detail selector-item matrix 442has a binary “0.”

The method moves on to look for detail selectors indirectly associatedwith i₁ 426 (i.e. the next object associated with the current item i₁ isan entity thereby indirectly associating the current item with detailselectors, at query 212). The method does so by traversing each entitydirectly associated with i₁ 426 using the entity-item matrix 440. Themethod finds that e₁ 414 and e₂ 416 are directly associated with i₁ 426because each of the memory slots at rows 1 and 2 and column 1 (memoryslots [1,1] and [2,1]) in the entity-item matrix 440 has a binary “1.”The method then iterates 208 to each of these entities e₁ 414 and e₂ 416and through 214 each detail selector directly associated with each ofthese entities e₁ 414 and e₂ 416. In this example, e₁ 414 directlyassociates with d₁ 402, d₂ 404, and d₃ 406, and e₂ 416 directlyassociates with d₁ 402 and d₂ 404. This is because there is a binary “1”in the detail selector-entity matrix 438 for each of memory slots [1,1],[2,1], and [3,1], as well as [1,2] and [2,2].

Beginning with d₁ 402 (traversing from e₁ 414), the method compares 216d ₁'s contributing item field in DSIC table 446 with the current itemID, which is i₁'s identification (not illustrated in figure). Detailselector d₁'s contributing item field 450 is presently “NULL” (notillustrated) because d₁ 402 has not yet been traversed; theinitialization step 202 ensures that all detail selectors have “0” itemcounts/tallies and “NULL” contributing item fields in the beginning.“NULL” is different from i₁'s identification and as such d₁'s itemcount/tally 448 is incremented 218 to a numerical “1” and itscontributing item field 450 updated to (i.e. replaced with) theidentification of i₁ (not illustrated). The method continues looping214, 216, 218, 220, through all detail selectors associated with entitye₁ (in this example, d₁, d₂, d₃). Following that, it repeats the wholeprocess for the next entity (object) 208, and then the next item 206,until all items in the matching set have been traversed. At that pointthe method has completed 226 the counting of the numbers of matchingitems associated with each detail selector and is ready to provide thisdata.

Moreover, the method can be used initially, with the NULL or emptyquery, to determine and display the initial item counts associated withevery selector in the data. As a user chooses selectors to add to thequery, the system automatically creates a Boolean expression comprisedof the chosen selectors, determines the matching items and performingthe described steps to determine the matching item counts associatedwith each selector. Usually the selectors with zero counts are notdisplayed, marked distinctively, and/or disabled.

The above description of item counting methods is but one possibility.The following describes another, which for reference we will call thesingle matrix method. This method creates a matrix of (indirect) item todetail selector associations.

Single Matrix Method

In order to make the calculation of associated item counts both simplerand faster, in some embodiments, a single matrix method can be used.This improves the performance quite appreciably, at the expense of someadditional pre-processing time needed to create the single matrix fromthe multiple matrices. However, this binary matrix product can beevaluated during initialization when, for some database applications,the time spent may be quite acceptable.

In matrix language, referring to FIG. 4 examples, the required singlematrix is the result of the matrix product of the entity-item matrix 440and the detail selector-entity matrix 438. The result is the productmatrix (or Indirect Association Detail Selector-Item) 448 (see FIG. 5).One method of evaluating this product matrix begins by traversing eachitem-to-entity vector, in the example columns 426, 428, 430, 432, 434,436 in entity-item matrix 440 in FIG. 4. Its components are theassociated entities, in the example 414, 416, 418, 420, 422, 424. Eachentity component in turn corresponds to an entity-to-detail selectorvector (columns 414, 416, 418, 420, 422, 424) in the detailselector-entity matrix 438, each of these vectors has detail selectorsas components and are checked for at least one common component. In theexample, the first column item-to-entity vector 426 in matrix 440 wouldhave the entities e₁, e₂, as components. The first row detailselector-to-entity vector 402 in matrix 438 would have the entitycomponents e₁, e₂, e₅. If at least one of the entity components ispresent in both vectors (in the example, e₁ and e₂ are present in both),then the association between the item (i₁) and the detail selector (d₁)exists and a binary “1” is entered into the corresponding cell (e.g.[1,1]) of the product matrix 448 (FIG. 5), otherwise it does not and azero is entered in the corresponding cell of the product matrix. In thismethod the checking of common entity components can terminate after thefirst common one is encountered.

Alternatively, and in most cases more optimally, the same two matrices440 and 438 are used. Traverse the item-to-entity vectors (item vectors)in example matrix 440 (columns 426, 428, 430, 432, 434, 436). For eachitem vector, determine the disjunction of every entity-to-detailselector corresponding to each item vector's entity component. That is,determine the union set of detail selectors associated with every entityvector corresponding to the component entities in the current itemvector in example matrix 438 (for item i₁, the component entities aree₁, e₂). The resulting union set of detail selectors are the componentsof the current item-to-detail selector vector corresponding to thecurrent item (i₁). The principal method step in this process is theevaluation of the disjunction between two entity vectors. This is theelemental method step used in calculating the disjunction of many entityvectors as follows. The first vector is disjoined with the next one andthe result placed in the longer vector of the two. Then this resultvector is disjoined with the next vector and so on until all vectorshave been disjoined. The last vector holds the result set of detailselectors which is the set of components of the current item vector. Thefollowing are further details of one possible method of carrying outthis elemental step. There are of course other possible methods so thatthis method is not meant to be limiting in any way.

The two entity vectors (in the example when the current item vector isi₁, in matrix 438 in FIG. 4, e₁=[d₁, d₂, d₃] and e₂=[d₁, d₂]) areimplemented in two different forms: the longer of the two (e₁ in theexample) would be implemented as (or converted to) a bit vector or bitmap (example e₁=111000) whereas the shorter of the two would be (remain)an array of IDs (row numbers) of the associated detail selectors, thatis an ID vector (in the example, e₂=[1, 2]). Then the disjunction can becarried out and stored in the bitmap vector, by using the components ofthe shorter vector (1, 2) to address each corresponding bit position inthe bitmap vector and write a 1 to it (without the need to check if itis 1 already). In this example, the 1st and the 2nd bits of the bitmapvector would be written to, even though they are already 1. Thisoperation is very fast. In this example, the result would be which, inthis very small example, accidentally happens to be the same as the e₁vector. An example of the procedure steps for evaluation of the resultof the disjunction of two (entity) vectors, corresponding to the firsttwo components of the current item, is as follows:

1 Start with two entity vectors in ID representation2 Convert the longer of the two entity vectors to bitmap representation(bit vector)3 Iterate through each component of the (shorter) entity vector and useeach component to turn on the corresponding bit in the bit vector4 Dispose of the shorter vector and replace it with the next vector inthe ID representation5 Iterate through each component of the current vector and use eachcomponent to turn on the corresponding bit in the bit vector6 Repeat from step 4 until finished.

The above procedure is evaluated for each item in the data set. Theoutput of the above is a bitmap representation of a set of detailselectors associated with the current item. Therefore it represents thecomponents of the item-to-entity vector for the current item. Repeatingthe process for each item results in the item-to-entity vector which canthen be used to determine the counts of matching items associated witheach selector. This is a simple counting method where a table ofselector IDs holds the current count for each selector and this count isincremented each time the respective selector is visited when traversingthe selectors associated with each matching item.

Extension to More General Applications

Both the single matrix and the double matrix methods can be easilyextended to the case of multiple levels of entities. The single matrixmethod can be extended by repeating the iteration through the entitiesfor each subsequent additional layer of entities.

Both methods can also be used to just determine the items associatedwith a given detail selector, rather than to determine item counts. Moregenerally, items, entities and detail selectors can be any set ofrelated objects where the relations between them can be representedsimilarly to those between items, entities, and detail selectors.

One example of slightly more general objects is the case of animals (forexample, horses) each represented by an object. Then the first level ofobjects could be the first generation of a set of animals. The secondlevel of objects, each connected to its parents at the first level,would be the next generation of the animals. The third level objects,each connected to its parent at the second level, would then be thethird generation. And so on. Then in the case of just three levels, itemcounting method would count the number of (or the list of) thirdgeneration offspring from each first generation parent. Relationshipsbetween people communicating on social web sites is another example.

These variations shall be discussed herein as the various embodimentsare set forth. The disclosure now turns to FIG. 6. With reference toFIG. 6, an exemplary system 600 includes a general-purpose computingdevice 600, including a processing unit (CPU or processor) 620 and asystem bus 610 that couples various system components including thesystem memory 630 such as read only memory (ROM) 640 and random accessmemory (RAM) 650 to the processor 620. The system 600 can include acache of high speed memory connected directly with, in close proximityto, or integrated as part of the processor 620. The system 600 copiesdata from the memory 630 and/or the storage device 660 to the cache forquick access by the processor 620. In this way, the cache provides aperformance boost that avoids processor 620 delays while waiting fordata. These and other modules can control or be configured to controlthe processor 620 to perform various actions. Other system memory 630may be available for use as well. The memory 630 can include multipledifferent types of memory with different performance characteristics. Itcan be appreciated that the disclosure may operate on a computing device600 with more than one processor 620 or on a group or cluster ofcomputing devices networked together to provide greater processingcapability. The processor 620 can include any general purpose processorand a hardware module or software module, such as module 1 662, module 2664, and module 3 666 stored in storage device 660, configured tocontrol the processor 620 as well as a special-purpose processor wheresoftware instructions are incorporated into the actual processor design.The processor 620 may essentially be a completely self-containedcomputing system, containing multiple cores or processors, a bus, memorycontroller, cache, etc. A multi-core processor may be symmetric orasymmetric.

The system bus 610 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 140 or the like, may provide the basicroutine that helps to transfer information between elements within thecomputing device 600, such as during start-up. The computing device 600further includes storage devices 660 such as a hard disk drive, amagnetic disk drive, an optical disk drive, tape drive or the like. Thestorage device 660 can include software modules 662, 664, 666 forcontrolling the processor 620. Other hardware or software modules arecontemplated. The storage device 660 is connected to the system bus 610by a drive interface. The drives and the associated computer readablestorage media provide nonvolatile storage of computer readableinstructions, data structures, program modules and other data for thecomputing device 600. In one aspect, a hardware module that performs aparticular function includes the software component stored in anon-transitory computer-readable medium in connection with the necessaryhardware components, such as the processor 620, bus 610, display 670,and so forth, to carry out the function. The basic components are knownto those of skill in the art and appropriate variations are contemplateddepending on the type of device, such as whether the device 600 is asmall, handheld computing device, a desktop computer, or a computerserver.

Although the exemplary embodiment described herein employs the hard disk660, it should be appreciated by those skilled in the art that othertypes of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, solid state drives, randomaccess memories (RAMs) 650, read only memory (ROM) 640, a cable orwireless signal containing a bit stream and the like, may also be usedin the exemplary operating environment. Non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

To enable user interaction with the computing device 600, an inputdevice 690 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 670 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems enable a user to provide multiple types of input to communicatewith the computing device 600. The communications interface 680generally governs and manages the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

For clarity of explanation, the illustrative system embodiment ispresented as including individual functional blocks including functionalblocks labeled as a “processor” or processor 620. The functions theseblocks represent may be provided through the use of either shared ordedicated hardware, including, but not limited to, hardware capable ofexecuting software and hardware, such as a processor 620, that ispurpose-built to operate as an equivalent to software executing on ageneral purpose processor. For example the functions of one or moreprocessors presented in FIG. 6 may be provided by a single sharedprocessor or multiple processors. (Use of the term “processor” shouldnot be construed to refer exclusively to hardware capable of executingsoftware.) Illustrative embodiments may include microprocessor and/ordigital signal processor (DSP) hardware, read-only memory (ROM) 640 forstoring software performing the operations discussed below, and randomaccess memory (RAM) 650 for storing results. Very large scaleintegration (VLSI) hardware embodiments, as well as custom VLSIcircuitry in combination with a general purpose DSP circuit, may also beprovided.

The logical operations of the various embodiments are implemented as:(1) a sequence of computer implemented steps, operations, or proceduresrunning on a programmable circuit within a general use computer, (2) asequence of computer implemented steps, operations, or proceduresrunning on a specific-use programmable circuit; and/or (3)interconnected machine modules or program engines within theprogrammable circuits. The system 600 shown in FIG. 6 can practice allor part of the recited methods, can be a part of the recited systems,and/or can operate according to instructions in the recitednon-transitory computer-readable storage media. Such logical operationscan be implemented as modules configured to control the processor 620 toperform particular functions according to the programming of the module.For example, FIG. 6 illustrates three modules Mod1 662, Mod2 664 andMod3 666 which are modules configured to control the processor 620.These modules may be stored on the storage device 660 and loaded intoRAM 650 or memory 630 at runtime or may be stored as would be known inthe art in other computer-readable memory locations.

Embodiments within the scope of the present disclosure may also includetangible and/or non-transitory computer-readable storage media forcarrying or having computer-executable instructions or data structuresstored thereon. Such non-transitory computer-readable storage media canbe any available media that can be accessed by a general purpose orspecial purpose computer, including the functional design of any specialpurpose processor as discussed above. By way of example, and notlimitation, such non-transitory computer-readable media can include RAM,ROM, EEPROM, CD-ROM, Solid State Drive, or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to carry or store desired program code means inthe form of computer-executable instructions, data structures, orprocessor chip design. When information is transferred or provided overa network or another communications connection (either hardwired,wireless, or combination thereof) to a computer, the computer properlyviews the connection as a computer-readable medium. Thus, any suchconnection is properly termed a computer-readable medium. Combinationsof the above should also be included within the scope of thecomputer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,components, data structures, objects, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Those of skill in the art will appreciate that other embodiments of thedisclosure may be practiced in network computing environments with manytypes of computer system configurations, including personal computers,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Embodiments may also be practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination thereof) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. Those skilled in the art will readily recognize variousmodifications and changes that may be made to the principles describedherein without following the example embodiments and applicationsillustrated and described herein, and without departing from the spiritand scope of the disclosure.

I claim:
 1. A computer-implemented method comprising: uniquelyidentifying, using a computing device, one or more items, from aplurality of items in a database stored in a computer system, theuniquely identified items having an indirect association with a detailselector; and counting, using the computing device, the uniquelyidentified items resulting in a count of uniquely identified itemsindirectly associated with the detail selector.
 2. Thecomputer-implemented method of claim 1, wherein the indirect associationrelates the detail selector to the identified items by virtue of mutualrelationships between the detail selector and the identified items,respectively, to a common entity.
 3. The computer-implemented method ofclaim 2, wherein the entity represents an intermediate relationshipbetween the selected detail selector and the identified items.
 4. Thecomputer-implemented method of claim 1, further comprising: providing alist of available detail selectors that includes at least the detailselector, the available detail selectors being available for selection;updating the list of available detail selectors in response to aselection of one of the detailed selectors, the updating resulting in alist of now available detail selectors including detail selectors whichcan be combined in a query with the selected detail selector, the resultof each query matching one or more of the identified items.
 5. Thecomputer-implemented method of claim 1, wherein the counting of theuniquely identified items further comprises: counting a first identifieditem; retaining an identification of the first identified item; countingany second identified item not having the identification; and totalingthe counts of each first identified item and each second identified itemto result in a total count of the identified items.
 6. Thecomputer-implemented method of claim 1, wherein the counting of theitems further comprises: counting each of the one or more items in thedatabase only once.
 7. The computer-implemented method of claim 1,wherein the counting of the uniquely identified items further comprises:determining if a uniquely identified item associated with a secondentity has already contributed to the count by virtue of the uniquelyidentified item's association with a first entity, and when suchdetermination is made ignoring the unique item associated with a secondentity for the count.
 8. The computer-implemented method of claim 1,wherein the indirect association of detail selector and the identifieditem is through a direct association of a common entity.
 9. Thecomputer-implemented method of claim 1, further comprising: subsequentto identifying the items having an indirect association with a selecteddetail selector, performing a reverse entity search comprising the itemsand a disjunctive Boolean operator, the search resulting in anidentification of all entities matching the reverse entity search. 10.The computer-implemented method of claim 9, further comprising:subsequent to identifying the entities matching the reverse entitysearch, performing a reverse detail selector search comprising theentities matching the reverse entity search and a disjunctive Booleanoperator, the search resulting in an identification of all detailselectors matching the reverse detail selector search.
 11. Thecomputer-implemented method of claim 10, wherein identifying the one ormore items in the computer system comprises: performing a forward entitysearch comprising detail selectors and a Boolean operator, the searchresulting in an identification of entities matching the forward entitysearch; performing a forward item search comprising the entitiesmatching the forward entity search and a disjunctive Boolean operator,the search resulting in an identification of items matching the forwarditem search, thereby the uniquely identified items in the computersystem are identified.
 12. The computer-implemented method of claim 1,wherein the counting further comprises: creating a product matrix; andcounting the items associated with each detail selector.
 13. Thecomputer-implemented method of claim 12, wherein the product matrix isthe result of a binary product of a detail-selector-to-entity matrix andan entity-to-item matrix wherein each binary sum is treated as a Booleandisjunction and each product as a Boolean conjunction.
 14. Acomputer-readable medium having computer-readable instructions storedthereon effective for causing a computer to perform a method comprising:identifying, using a computing device, one or more items in a databasestored in a computer system, the items having an indirect associationwith a detail selector; and counting, using the computing device, theidentified items resulting in a count of identified items indirectlyassociated with the detail selector.
 15. The computer-readable medium ofclaim 14, wherein the indirect association relates the detail selectorto the identified items by virtue of mutual relationships between thedetail selector and the identified items, respectively, to a commonentity.
 16. The computer-readable medium of claim 14, wherein thecounting of the identified items further comprises: counting a firstidentified item; retaining an identification of the first identifieditem; counting any second identified item not having the identification;and totaling the counts of each first identified item and each secondidentified item to result in a total count of the identified items. 17.The computer-readable medium of claim 14, further comprising: subsequentto identifying the items having an indirect association with a selecteddetail selector, performing a reverse entity search comprising theuniquely identified items and a disjunctive Boolean operator, the searchresulting in an identification of all entities matching the reverseentity search.
 18. The computer-readable medium of claim 17, furthercomprising: subsequent to identifying the entities matching the reverseentity search, performing a reverse detail selector search comprisingthe entities matching the reverse entity search and a disjunctiveBoolean operator, the search resulting in an identification of alldetail selectors matching the reverse detail selector search.
 19. Thecomputer-readable medium of claim 18, wherein identifying the one ormore items in the computer system comprises: performing a forward entitysearch comprising detail selectors and a Boolean operator, the searchresulting in an identification of entities matching the forward entitysearch; performing a forward item search comprising the entitiesmatching the forward entity search and a disjunctive Boolean operator,the search resulting in an identification of items matching the forwarditem search, thereby the items in the computer system are identified.20. The computer-readable medium of claim 14, wherein the countingfurther comprises: creating a product matrix; and counting the itemsassociated with each detail selector.